Neuroimaging in schizophrenia: A review article

A Hidden Mark of a Troubled Past: Neuroimaging and Transcriptomic Analyses Reveal Interactive Effects of Maternal Immune Activation and Adolescent THC Exposure Suggestive of Increased Neuropsychiatric Risk

Background

Maternal exposure to infections during gestation has been shown to predispose individuals to neuropsychiatric disorders. Additionally, clinical data suggest that cannabis use may trigger the onset of schizophrenia in vulnerable individuals. However, the direction of causality remains unclear.

Methods

To elucidate this issue, we utilized a rat model of maternal immune activation combined with exposure to increasing doses of Δ9-tetrahydrocannabinol during adolescence in both male and female rats. We investigated several behaviors in adulthood relevant for neuropsychiatric disorders, including impairments in working memory, deficits in sensorimotor gating, alterations in social behavior, anhedonia, and potential changes in implicit learning (conditioned taste aversion). Furthermore, we conducted a longitudinal positron emission tomography study to target affected brain regions and, subsequently, collected brain samples of one such region (the orbitofrontal cortex) for RNA sequencing analyses, which were also performed on peripheral blood mononuclear cells to identify peripheral biomarkers.

Results

While adolescent Δ9-tetrahydrocannabinol did not unmask latent behavioral disruptions, positron emission tomography scans revealed several brain alterations dependent on the combination of both hits. Additionally, the transcriptomic studies demonstrated that maternal immune activation affected dopaminergic, glutamatergic, and serotoninergic genes, with the combination of both exposures in most cases shifting the expression from downregulation to upregulation. In peripheral cells, interactive effects were observed on inflammatory pathways, and some genes were proposed as biomarkers.

Conclusions

These results suggest that the combination of these 2 vulnerability factors leaves a lasting mark on the body, potentially predisposing individuals to neuropsychiatric disorders even before behavioral alterations manifest.

Advancing treatment response prediction in first-episode psychosis: integrating clinical and electroencephalography features

AIMS

Prompt diagnosis and intervention are crucial for first-episode psychosis (FEP) outcomes, but predicting the response to antipsychotics remains challenging. We studied whether adding electroencephalography (EEG) characteristics improves clinical prediction models for treatment response and whether EEG-based predictors are influenced by initial treatment.

METHODS

We included 115 antipsychotic-naïve patients with FEP. Positive and Negative Syndrome Scale (PANSS) and sociodemographic items were included as clinical features. Additionally, we analyzed resting-state EEG data (n = 45) for (relative) power, functional connectivity, and network organization. Treatment response, measured as change in PANSS positive subscale scores (∆PANSS+), was predicted using a random forest regression model. We analyzed whether the most predictive EEG characteristics were influenced after treatment.

RESULTS

The clinical model explained 12% variance in symptom reduction in the training set and 32% in the validation set. Including EEG variables in the model led to a nonsignificant increase of 2% (total 34%) explained variance in symptom reduction. High hallucination symptom scores and a more hierarchical organization of alpha band networks (tree hierarchy) were associated with ∆PANSS+ reduction. The tree hierarchy in the alpha band decreased after medication. EEG source analysis revealed that this change was driven by alterations in the degree and centrality of frontal and parietal nodes in the functional brain network.

CONCLUSIONS

Both clinical and EEG characteristics can inform treatment response prediction in patients with FEP, but the combined model may not be beneficial over a clinical model. Nevertheless, adding a more objective marker such as EEG could be valuable in selected cases.

Measurement of Limbic System Anatomical Volumes in Patients Diagnosed with Schizophrenia Using Vol2brain and Comparison with Healthy Individuals

Background and Objectives: Schizophrenia is a chronic psychiatric disorder affecting approximately 24 million people worldwide, characterized by structural and functional brain abnormalities. Despite its prevalence, automated segmentation tools like Vol2Brain have been underutilized in large-sample studies examining limbic system anatomical volumes in patients with schizophrenia. This study aimed to assess volume differences in all major limbic system structures between schizophrenia patients and healthy controls using Vol2Brain. Method: This retrospective study included 68 schizophrenia patients and 68 healthy controls, with MRI scans obtained from OpenNeuro. Limbic system volumetric and cortical thickness measurements were conducted using Vol2Brain, an automated segmentation platform. Results: Schizophrenia patients exhibited significantly reduced volumes in the amygdala, hippocampus, anterior cingulate gyrus, posterior cingulate gyrus, and middle cingulate gyrus compared to controls. However, the left amygdala volume was larger in schizophrenia patients. A cortical thickness analysis revealed that schizophrenia patients had thinner limbic cortices, particularly in the anterior and posterior cingulate gyri and the right parahippocampal gyrus. In contrast, the right anterior cingulate gyrus was thicker in schizophrenia patients. The differences in total and left parahippocampal gyrus volumes and cortical thickness did not reach statistical significance. Conclusions: These findings reinforce previous evidence of limbic system abnormalities in patients with schizophrenia, which may contribute to cognitive and emotional dysregulation. The study also highlights Vol2Brain's potential as a rapid, cost-free, and reliable alternative for brain volume analysis, facilitating more standardized and reproducible neuroimaging assessments in psychiatric research.

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade

BACKGROUND AND HYPOTHESIS

Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years.

STUDY DESIGN

The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade.

STUDY RESULTS

Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders.

CONCLUSIONS

We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.

Brain neuromarkers predict self- and other-related mentalizing across adult, clinical, and developmental samples

Human social interactions rely on the ability to reflect on one's own and others' internal states and traits-a psychological process known as mentalizing. Impaired or altered self- and other-related mentalizing is a hallmark of multiple psychiatric and neurodevelopmental conditions. Yet, replicable and easily testable brain markers of mentalizing have so far been lacking. Here, we apply an interpretable machine learning approach to multiple datasets (total N=281) to train and validate fMRI brain signatures that predict 1) mentalizing about the self, 2) mentalizing about another person, and 3) both types of mentalizing. We test their generalizability across healthy adults, adolescents, and adults diagnosed with schizophrenia and bipolar disorder. The classifier trained across both types of mentalizing showed 98% predictive accuracy in independent validation datasets. Self-mentalizing and other-mentalizing classifiers had positive weights in anterior/medial and posterior/lateral brain areas respectively, with accuracy rates of 82% and 77% for out-of-sample prediction. Classifier patterns across cohorts revealed better self/other separation in 1) healthy adults compared to individuals with schizophrenia and 2) with increasing age in adolescence. Together, our findings reveal consistent and separable neural patterns subserving mentalizing about self and others-present at least from the age of adolescence and functionally altered in severe neuropsychiatric disorders. These mentalizing signatures hold promise as mechanistic neuromarkers to measure social-cognitive processes in different contexts and clinical conditions.

Heterozygosity for neurodevelopmental disorder-associated TRIO variants yields distinct deficits in behavior, neuronal development, and synaptic transmission in mice

Genetic variants in TRIO are associated with neurodevelopmental disorders (NDDs) including schizophrenia (SCZ), autism spectrum disorder (ASD) and intellectual disability. TRIO uses its two guanine nucleotide exchange factor (GEF) domains to activate GTPases (GEF1: Rac1 and RhoG; GEF2: RhoA) that control neuronal development and connectivity. It remains unclear how discrete TRIO variants differentially impact these neurodevelopmental events. Here, we investigate how heterozygosity for NDD-associated Trio variants - +/K1431M (ASD), +/K1918X (SCZ), and +/M2145T (bipolar disorder, BPD) - impact mouse behavior, brain development, and synapse structure and function. Heterozygosity for different Trio variants impacts motor, social, and cognitive behaviors in distinct ways that model clinical phenotypes in humans. Trio variants differentially impact head and brain size, with corresponding changes in dendritic arbors of motor cortex layer 5 pyramidal neurons (M1 L5 PNs). Although neuronal structure was only modestly altered in the Trio variant heterozygotes, we observe significant changes in synaptic function and plasticity. We also identified distinct changes in glutamate synaptic release in +/K1431M and +/M2145T cortico-cortical synapses. The TRIO K1431M GEF1 domain has impaired ability to promote GTP exchange on Rac1, but +/K1431M mice exhibit increased Rac1 activity, associated with increased levels of the Rac1 GEF Tiam1. Acute Rac1 inhibition with NSC23766 rescued glutamate release deficits in +/K1431M variant cortex. Our work reveals that discrete NDD-associated Trio variants yield overlapping but distinct phenotypes in mice, demonstrates an essential role for Trio in presynaptic glutamate release, and underscores the importance of studying the impact of variant heterozygosity in vivo.

Preoperative mapping techniques for brain tumor surgery: a systematic review

Accurate preoperative mapping is crucial for maximizing tumor removal while minimizing damage to critical brain functions during brain tumor surgery. Navigated transcranial magnetic stimulation (nTMS), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) are established methods for assessing motor and language function. Following PRISMA guidelines, this systematic review analyzes the reliability, clinical utility, and accessibility of these techniques. A total of 128 studies (48 nTMS, 56 fMRI, 24 MEG) were identified from various databases. The analysis finds nTMS to be a safe, standardized method with high accuracy compared to direct cortical stimulation for preoperative motor mapping. Combining nTMS with tractography allows for preoperative assessment of short-term and long-term motor deficits, which may not be possible with fMRI. fMRI data interpretation requires careful consideration of co-activated, non-essential areas (potentially leading to false positives) and situations where neural activity and blood flow are uncoupled (potentially leading to false negatives). These limitations restrict fMRI's role in preoperative planning for both motor and language functions. While MEG offers high accuracy in motor mapping, its high cost and technical complexity contribute to the limited number of available studies. Studies comparing preoperative language mapping techniques with direct cortical stimulation show significant variability across all methods, highlighting the need for larger, multicenter studies for validation. Repetitive nTMS speech mapping offers valuable negative predictive value, allowing clinicians to evaluate whether a patient should undergo awake or asleep surgery. Language function monitoring heavily relies on the specific expertise and experience available at each center, making it challenging to establish general recommendations.

Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms

BACKGROUND AND OBJECTIVE

The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling timely interventions, and optimizing treatment plans. Functional connectivity analysis, utilizing functional magnetic resonance imaging data, has been demonstrated to offer invaluable biomarkers conducive to clinical diagnosis. However, previous studies mainly focus on traditional machine learning methods or hand-crafted neural networks, which may not fully capture the spatial topological relationship between brain regions.

METHODS

This paper proposes an evolutionary algorithm (EA) based graph neural architecture search (GNAS) method. EA-GNAS has the ability to search for high-performance graph neural networks for schizophrenia spectrum disorder prediction. Moreover, we adopt GNNExplainer to investigate the explainability of the acquired architectures, ensuring that the model's predictions are both accurate and comprehensible.

RESULTS

The results suggest that the graph neural network model, derived using genetic algorithm search, outperforms under five-fold cross-validation, achieving a fitness of 0.1850. Relative to conventional machine learning and other deep learning approaches, the proposed method yields superior accuracy, F1 score, and AUC values of 0.8246, 0.8438, and 0.8258, respectively.

CONCLUSION

Based on a multi-site dataset from schizophrenia spectrum disorder patients, the findings reveal an enhancement over prior methods, advancing our comprehension of brain function and potentially offering a biomarker for diagnosing schizophrenia spectrum disorder.

Estrogen and psychosis - a review and future directions

The link between sex hormones and schizophrenia has been suspected for over a century; however, scientific evidence supporting the pharmacotherapeutic effects of exogenous estrogen has only started to emerge during the past three decades. Accumulating evidence from epidemiological and basic research suggests that estrogen has a protective effect in women vulnerable to schizophrenia. Such evidence has led multiple researchers to investigate the role of estrogen in schizophrenia and its use in treatment. This narrative review provides an overview of the effects of estrogen as well as summarizes the recent work regarding estrogen as a treatment for schizophrenia, particularly the use of new-generation selective estrogen receptor modulators.

Schizophrenia Detection and Classification: A Systematic Review of the Last Decade

BACKGROUND/OBJECTIVES

Artificial Intelligence (AI) in healthcare employs advanced algorithms to analyze complex and large-scale datasets, mimicking aspects of human cognition. By automating decision-making processes based on predefined thresholds, AI enhances the accuracy and reliability of healthcare data analysis, reducing the need for human intervention. Schizophrenia (SZ), a chronic mental health disorder affecting millions globally, is characterized by symptoms such as auditory hallucinations, paranoia, and disruptions in thought, behavior, and perception. The SZ symptoms can significantly impair daily functioning, underscoring the need for advanced diagnostic tools.

METHODS

This systematic review has been conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines and examines peer-reviewed studies from the last decade (2015-2024) on AI applications in SZ detection as well as classification. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number: CRD42024612364. Research has been sourced from multiple databases and screened using predefined inclusion criteria. The review evaluates the use of both Machine Learning (ML) and Deep Learning (DL) methods across multiple modalities, including Electroencephalography (EEG), Structural Magnetic Resonance Imaging (sMRI), and Functional Magnetic Resonance Imaging (fMRI). The key aspects reviewed include datasets, preprocessing techniques, and AI models.

RESULTS

The review identifies significant advancements in AI methods for SZ diagnosis, particularly in the efficacy of ML and DL models for feature extraction, classification, and multi-modal data integration. It highlights state-of-the-art AI techniques and synthesizes insights into their potential to improve diagnostic outcomes. Additionally, the analysis underscores common challenges, including dataset limitations, variability in preprocessing approaches, and the need for more interpretable models.

CONCLUSIONS

This study provides a comprehensive evaluation of AI-based methods in SZ prognosis, emphasizing the strengths and limitations of current approaches. By identifying unresolved gaps, it offers valuable directions for future research in the application of AI for SZ detection and diagnosis.

Inferring social signals from the eyes in male schizophrenia

Nonverbal communication habitually leaks out in ways that expose underlying thoughts, true feelings, and integrity of a counterpart. Social cognition is deficient in a wide range of mental disorders, including schizophrenia (SZ). Inferring social signals through the eyes is pivotal for social interaction but remains poorly investigated. The present work aims to fill this gap by examining whether and, if so, how reading language of the eyes is altered in SZ. We focused on male SZ, primarily because the disorder manifests a gender-specific profile. Patients and matched typically developing (TD) individuals were administered the Reading the Mind in the Eyes Test-Modified (RMET-M) and Emotions in Masked Faces (EMF) task that provide comparable visual information. The findings indicate that in SZ, the emotion recognition profile is similar to TD, with a more accurate recognition of some emotions such as fear, neutral expressions, and happiness than the others (sadness and disgust). In SZ, however, this profile is shifted down: all emotions are recognized less accurately than in TD. On the RMET-M, patients are also less precise, albeit they perform better on items with positive valence. In SZ only, recognition accuracy on both tasks is tightly linked to each other. The outcome reveals global challenges for males with SZ in inferring social information in the eyes and calls for remediation programs to shape social cognition. This work offers novel insights into the profiles of social cognitive deficits in mental disorders that differ in their gender prevalence.

Neurostructural changes in schizophrenia and treatment-resistance: a narrative review

Schizophrenia is a complex disorder characterized by multiple neurochemical abnormalities and structural changes in the brain. These abnormalities may begin before recognizable clinical symptoms appear and continue as a dynamic process throughout the illness. Recent advances in imaging techniques have significantly enriched our comprehension of these structural alterations, particularly focusing on gray and white matter irregularities and prefrontal, temporal, and cingulate cortex alterations. Some of the changes suggest treatment resistance to antipsychotic medications, while treatment nonadherence and relapses may further exacerbate structural abnormalities. This narrative review aims to discuss the literature about alterations and deficits within the brain, which could improve the understanding of schizophrenia and how to interpret neurostructural changes.

ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns

Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.

Investigating bilirubin concentrations in schizophrenia: A systematic review and meta-analysis

Schizophrenia, a severe mental disorder characterized by chronic disability and poor quality of life, has been shown to be associated with alterations in redox balance. Recent research has suggested a potential link between the antioxidant bilirubin and schizophrenia, although findings have been inconsistent. To address this gap, we conducted a systematic review and meta-analysis to evaluate possible alterations in bilirubin concentrations in schizophrenia. A comprehensive search of major databases was conducted to identify articles reporting total and unconjugated bilirubin in schizophrenic patients and healthy controls in case-control studies. Our meta-analysis included 18 studies investigating 16,245 participants. The pooled results did not reveal any significant association between schizophrenia and total bilirubin concentrations. Additionally, such effect was strongly influenced by the results of a single study in sensitivity analysis. Subgroup and meta-regression analyses based on various factors such as study design, sample size, and geographical region showed no significant associations with the effect size, nor they identified sources of heterogeneity. Furthermore, publication bias assessments were conducted to ensure the robustness of the findings. Overall, our findings summarize the available evidence regarding the possible role of bilirubin as a biomarker of schizophrenia and highlight the importance of conducting further research in this area.

Implications of altered pyramidal cell morphology on clinical symptoms of neurodevelopmental disorders

The prevalence of pyramidal cells (PCs) in the mammalian cerebral cortex underscore their value as they play a crucial role in various brain functions, ranging from cognition, sensory processing, to motor output. PC morphology significantly influences brain connectivity and plays a critical role in maintaining normal brain function. Pathological alterations to PC morphology are thought to contribute to the aetiology of neurodevelopmental disorders such as autism spectrum disorder (ASD) and schizophrenia. This review explores the relationship between abnormalities in PC morphology in key cortical areas and the clinical manifestations in schizophrenia and ASD. We focus largely on human postmortem studies and provide evidence that dendritic segment length, complexity and spine density are differentially affected in these disorders. These morphological alterations can lead to disruptions in cortical connectivity, potentially contributing to the cognitive and behavioural deficits observed in these disorders. Furthermore, we highlight the importance of investigating the functional and structural characteristics of PCs in these disorders to illuminate the underlying pathogenesis and stimulate further research in this area.

Effects of Adapted Physical Activity on White Matter Integrity in Patients with Schizophrenia

Schizophrenia is associated with changes in white matter (WM) integrity and with reduced life expectancy, in part because of the cardiometabolic side effects of antipsychotics. Physical activity (PA) has emerged as a candidate lifestyle intervention that is safe and effective. The study aimed to assess how an adapted PA program delivered remotely by web (e-APA) improved WM integrity in patients with schizophrenia (SZPs) and healthy controls (HCs) and to evaluate associations among WM integrity, cardiorespiratory fitness, and symptom severity. This longitudinal study was conducted over 16 weeks with 31 participants (18 SZPs and 13 HCs). Diffusion tensor imaging and tract-based spatial statistics were employed to assess WM integrity. Cardiorespiratory fitness was measured by maximal oxygen uptake (VO2max), and assessments for clinical symptoms included the Positive and Negative Syndrome Scale, Self-evaluation of Negative Symptoms and the Brief Negative Syndrome Scale (BNSS). Only the SZPs had significantly increased WM integrity after the e-APA program, with increased fractional anisotropy and decreased radial diffusivity in fasciculi involved in motor functions and language process. Furthermore, decreased negative symptoms assessed with BNSS were associated with greater WM integrity following the program. These findings suggest that e-APA may improve WM integrity abnormalities and support e-APA as a promising therapeutic strategy.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

Objective

fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging.

Methods

We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP).

Results

We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity.

Conclusion

Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics.

Significance

Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Associations among plasma markers for N-methyl-d-aspartate receptor hypofunction, redox dysregulation, and insufficient myelination in patients with schizophrenia

Background

Several hypotheses regarding the pathomechanisms of schizophrenia have been proposed. If schizophrenia is a unitary disease, then these pathological processes must be linked; however, if such links do not exist, schizophrenia may best be considered a group of disorders. Only a few studies have examined the relationships among these pathomechanisms. Herein, we examined the relationships among deficient myelination, NMDA receptor hypofunction, and metabolic dysregulation by measuring various plasma markers and examining their correlations.

Methods

Plasma samples were collected from 90 patients with schizophrenia and 68 healthy controls. Concentrations of nardilysin (N-arginine dibasic convertase, NRDC), a positive regulator of myelination, the NMDA receptor co-agonist d-serine and glycine, various additional amino acids related to NMDA receptor transmission (glutamate, glutamine, and l-serine), and homocysteine (Hcy), were measured. Concentrations were compared using independent samples t-test or logistic regression, and associations were evaluated using Pearson's correlation coefficients.

Results

Plasma glycine (t = 2.05, p = 0.042), l-serine (t = 2.25, p = 0.027), and homocysteine (t = 3.71, p < 0.001) concentrations were significantly higher in patients with schizophrenia compared to those in healthy controls. Logistic regression models using age, sex, smoking status, glutamine, glutamate, glycine, l-serine, d-serine, homocysteine, and NRDC as independent variables revealed significantly lower plasma d-serine (p = 0.024) and NRDC (p = 0.028), but significantly higher l-serine (p = 0.024) and homocysteine (p = 0.001) in patients with schizophrenia. Several unique correlations were found between NMDA receptor-related amino acids and NRDC in patients with schizophrenia compared to those in healthy controls, while no correlations were found between plasma homocysteine and other markers. No associations were found between plasma marker concentrations and disease status or cognitive function in patients with schizophrenia, except for a significant correlation between plasma glycine and full intelligence quotient.

Conclusion

Reduced myelination and NMDA receptor hypofunction may be related to pathological mechanisms in schizophrenia, while homocysteine dysregulation appears to be an independent pathological process. These results suggest that schizophrenia may be a group of disorders with unique or partially overlapping etiologies.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

Objective

fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging.

Methods

We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP).

Results

We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity.

Conclusion

Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics.

Significance

Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Convergent and divergent genes expression profiles associated with brain-wide functional connectome dysfunction in deficit and non-deficit schizophrenia

Deficit schizophrenia (DS) is a subtype of schizophrenia characterized by the primary and persistent negative symptoms. Previous studies have identified differences in brain functions between DS and non-deficit schizophrenia (NDS) patients. However, the genetic regulation features underlying these abnormal changes are still unknown. This study aimed to detect the altered patterns of functional connectivity (FC) in DS and NDS and investigate the gene expression profiles underlying these abnormal FC. The study recruited 82 DS patients, 96 NDS patients, and 124 healthy controls (CN). Voxel-based unbiased brain-wide association study was performed to reveal altered patterns of FC in DS and NDS patients. Machine learning techniques were used to access the utility of altered FC for diseases diagnosis. Weighted gene co-expression network analysis (WGCNA) was employed to explore the associations between altered FC and gene expression of 6 donated brains. Enrichment analysis was conducted to identify the genetic profiles, and the spatio-temporal expression patterns of the key genes were further explored. Comparing to CN, 23 and 20 brain regions with altered FC were identified in DS and NDS patients. The altered FC among these regions showed significant correlations with the SDS scores and exhibited high efficiency in disease classification. WGCNA revealed associations between DS/NDS-related gene expression and altered FC. Additionally, 22 overlapped genes, including 12 positive regulation genes and 10 negative regulation genes, were found between NDS and DS. Enrichment analyses demonstrated relationships between identified genes and significant pathways related to cellular response, neuro regulation, receptor binding, and channel activity. Spatial and temporal gene expression profiles of SCN1B showed the lowest expression at the initiation of embryonic development, while DPYSL3 exhibited rapid increased in the fetal. The present study revealed different altered patterns of FC in DS and NDS patients and highlighted the potential value of FC in disease classification. The associations between gene expression and neuroimaging provided insights into specific and common genetic regulation underlying these brain functional changes in DS and NDS, suggesting a potential genetic-imaging pathogenesis of schizophrenia.

Autophagy-related gene model as a novel risk factor for schizophrenia

Autophagy, a cellular process where cells degrade and recycle their own components, has garnered attention for its potential role in psychiatric disorders, including schizophrenia (SCZ). This study aimed to construct and validate a new autophagy-related gene (ARG) risk model for SCZ. First, we analyzed differential expressions in the GSE38484 training set, identifying 4,754 differentially expressed genes (DEGs) between SCZ and control groups. Using the Human Autophagy Database (HADb) database, we cataloged 232 ARGs and pinpointed 80 autophagy-related DEGs (AR-DEGs) after intersecting them with DEGs. Subsequent analyses, including metascape gene annotation, pathway and process enrichment, and protein-protein interaction enrichment, were performed on the 80 AR-DEGs to delve deeper into their biological roles and associated molecular pathways. From this, we identified 34 candidate risk AR-DEGs (RAR-DEGs) and honed this list to final RAR-DEGs via a constructed and optimized logistic regression model. These genes include VAMP7, PTEN, WIPI2, PARP1, DNAJB9, SH3GLB1, ATF4, EIF4G1, EGFR, CDKN1A, CFLAR, FAS, BCL2L1 and BNIP3. Using these findings, we crafted a nomogram to predict SCZ risk for individual samples. In summary, our study offers deeper insights into SCZ's molecular pathogenesis and paves the way for innovative approaches in risk prediction, gene-targeted diagnosis, and community-based SCZ treatments.

ROI-based analysis of diffusion indices in healthy subjects and subjects with deficit or non-deficit syndrome schizophrenia

We analyzed DTI data involving 22 healthy subjects (HC), 15 patients with deficit syndrome schizophrenia (DSZ), and 25 patients with non-deficit syndrome schizophrenia (NDSZ). We used a 1.5-T MRI scanner to collect diffusion-weighted images and T1 images, which were employed to correct distortions and deformations within the diffusion-weighted images. For 156 regions of interest (ROI), we calculated the average fractional anisotropy (FA), mean diffusion (MD), and radial diffusion (RD). Each ROI underwent a group-wise comparison using permutation F-test, followed by post hoc pairwise comparisons with Bonferroni correction. In general, we observed lower FA in both schizophrenia groups compared to HC (i.e., HC>(DSZ=NDSZ)), while MD and RD showed the opposite pattern. Notably, specific ROIs with reduced FA in schizophrenia patients included bilateral nucleus accumbens, left fusiform area, brain stem, anterior corpus callosum, left rostral and caudal anterior cingulate, right posterior cingulate, left thalamus, left hippocampus, left inferior temporal cortex, right superior temporal cortex, left pars triangularis and right lingual gyrus. Significantly, the right cuneus exhibited lower FA in the DSZ group compared to other groups ((HC=NDSZ)>DSZ), without affecting MD and RD. These results indicate that compromised neural integrity in the cuneus may contribute to the pathophysiological distinctions between DSZ and NDSZ.

Psychomotor slowing in schizophrenia is associated with cortical thinning of primary motor cortex: A three cohort structural magnetic resonance imaging study

Psychomotor slowing (PS) is characterized by slowed movements and lower activity levels. PS is frequently observed in schizophrenia (SZ) and distressing because it impairs performance of everyday tasks and social activities. Studying brain topography contributing to PS in SZ can help to understand the underlying neurobiological mechanisms as well as help to develop more effective treatments that specifically target affected brain areas. Here, we conducted structural magnetic resonance imaging (sMRI) of three independent cohorts of right-handed SZ patients (SZ#1: n = 72, SZ#2: n = 37, SZ#3: n = 25) and age, gender and education matched healthy controls (HC) (HC#1: n = 40, HC#2: n = 37, HC#3: n = 38). PS severity in the three SZ cohorts was determined using the Positive and Negative Syndrome Scale (PANSS) item #G7 (motor retardation) and Trail-Making-Test B (TMT-B). FreeSurfer v7.2 was used for automated parcellation and segmentation of cortical and subcortical regions. SZ#1 patients showed reduced cortical thickness in right precentral gyrus (M1; p = 0.04; Benjamini-Hochberg [BH] corr.). In SZ#1, cortical thinning in right M1 was associated with PANSS item #G7 (p = 0.04; BH corr.) and TMT-B performance (p = 0.002; BH corr.). In SZ#1, we found a significant correlation between PANSS item #G7 and TMT-B (p = 0.005, ρ=0.326). In conclusion, PANSS G#7 and TMT-B might have a surrogate value for predicting PS in SZ. Cortical thinning of M1 rather than alterations of subcortical structures may point towards cortical pathomechanism underlying PS in SZ.

Altered Associations Between Task Performance and Dorsolateral Prefrontal Cortex Activation During Cognitive Control in Schizophrenia

BACKGROUND

Dysfunctional cognitive control processes are now well understood to be core features of schizophrenia (SZ). A body of work suggests that the dorsolateral prefrontal cortex (DLPFC) plays a critical role in explaining cognitive control disruptions in SZ. Here, we examined relationships between DLPFC activation and drift rate (DR), a model-based performance measure that combines reaction time and accuracy, in people with SZ and healthy control (HC) participants.

METHODS

One hundred fifty-one people with recent-onset SZ spectrum disorders and 118 HC participants performed the AX-Continuous Performance Task during functional magnetic resonance imaging scanning. Proactive cognitive control-associated activation was extracted from left and right DLPFC regions of interest. Individual behavior was fit using a drift diffusion model, allowing DR to vary between task conditions.

RESULTS

Behaviorally, people with SZ showed significantly lower DRs than HC participants, particularly during high proactive control trial types ("B" trials). Recapitulating previous findings, the SZ group also demonstrated reduced cognitive control-associated DLPFC activation compared with HC participants. Furthermore, significant group differences were also observed in the relationship between left and right DLPFC activation with DR, such that positive relationships between DR and activation were found in HC participants but not in people with SZ.

CONCLUSIONS

These results suggest that DLPFC activation is less associated with cognitive control-related behavioral performance enhancements in SZ. Potential mechanisms and implications are discussed.

Deviant spontaneous neural activity as a potential early-response predictor for therapeutic interventions in patients with schizophrenia

Objective

Previous studies have established significant differences in the neuroimaging characteristics between healthy controls (HCs) and patients with schizophrenia (SCZ). However, the relationship between homotopic connectivity and clinical features in patients with SCZ is not yet fully understood. Furthermore, there are currently no established neuroimaging biomarkers available for the diagnosis of SCZ or for predicting early treatment response. The aim of this study is to investigate the association between regional homogeneity and specific clinical features in SCZ patients.

Methods

We conducted a longitudinal investigation involving 56 patients with SCZ and 51 HCs. The SCZ patients underwent a 3-month antipsychotic treatment. Resting-state functional magnetic resonance imaging (fMRI), regional homogeneity (ReHo), support vector machine (SVM), and support vector regression (SVR) were used for data acquisition and analysis.

Results

In comparison to HCs, individuals with SCZ demonstrated reduced ReHo values in the right postcentral/precentral gyrus, left postcentral/inferior parietal gyrus, left middle/inferior occipital gyrus, and right middle temporal/inferior occipital gyrus, and increased ReHo values in the right putamen. It is noteworthy that there was decreased ReHo values in the right inferior parietal gyrus after treatment compared to baseline data.

Conclusion

The observed decrease in ReHo values in the sensorimotor network and increase in ReHo values in the right putamen may represent distinctive neurobiological characteristics of patients with SCZ, as well as a potential neuroimaging biomarker for distinguishing between patients with SCZ and HCs. Furthermore, ReHo values in the sensorimotor network and right putamen may serve as predictive indicators for early treatment response in patients with SCZ.

Increased Functional Connectivity Involving the Parahippocampal Gyrus in Patients with Schizophrenia during Theory of Mind Processing: A Psychophysiological Interaction Study

BACKGROUND

Theory of Mind (ToM) is an ability to infer the mental state of others, which plays an important role during social events. Previous studies have shown that ToM deficits exist frequently in schizophrenia, which may result from abnormal activity in brain regions related to sociality. However, the interactions between brain regions during ToM processing in schizophrenia are still unclear. Therefore, in this study, we investigated functional connectivity during ToM processing in patients with schizophrenia, using functional magnetic resonance imaging (fMRI).

METHODS

A total of 36 patients with schizophrenia and 33 healthy controls were recruited to complete a ToM task from the Human Connectome Project (HCP) during fMRI scanning. Psychophysiological interaction (PPI) analysis was applied to explore functional connectivity.

RESULTS

Patients with schizophrenia were less accurate than healthy controls in judging social stimuli from non-social stimuli (Z = 2.31, p = 0.021), and displayed increased activity in the right inferior frontal gyrus and increased functional connectivity between the bilateral middle temporal gyrus and the ipsilateral parahippocampal gyrus during ToM processing (AlphaSim corrected p < 0.05).

CONCLUSIONS

Here, we showed that the brain regions related to sociality interact more with the parahippocampal gyrus in patients with schizophrenia during ToM processing, which may reflect a possible compensatory pathway of ToM deficits in schizophrenia. Our study provides a new idea for ToM deficits in schizophrenia, which could be helpful to better understand social cognition of schizophrenia.